Information dissemination refers to how information spreads among users on social
networks. With the widespread application of mobile communication and internet …

To obtain a foundational understanding of timeline algorithms and viral content in shaping
public opinions, computer scientists started to study augmented versions of opinion …

Causal inference in networks should account for interference, which occurs when a unit's
outcome is influenced by treatments or outcomes of peers. Heterogeneous peer influence …

Estimating how a treatment affects different individuals, known as heterogeneous treatment
effect estimation, is an important problem in empirical sciences. In the last few years, there …

Estimating the individual treatment effect (ITE) from observational data is a crucial research
topic that holds significant value across multiple domains. How to identify hidden …

Predicting information cascades holds significant practical implications, including
applications in public opinion analysis, rumor control, and product recommendation. Existing …

Behavioral diffusion is commonly modeled with the linear threshold model, which assumes
that individuals adopt a behavior when enough of their social contacts do so. We observe …

In Influence Maximization (IM), the objective is to--given a budget--select the optimal set of
entities in a network to target with a treatment so as to maximize the total effect. For instance …

Interference occurs when the potential outcomes of a unit depend on the treatments
assigned to other units. That is frequently the case in many domains, such as in the social …

The binary-decision dynamics of two types of individuals; coordinators who tend to choose
the more common option among others and anti-coordinators who avoid the common option …